Method and Apparatus for Registration of Different Mammography Image Views

ABSTRACT

A method of identifying potential lesions in mammographic images may include operations executed by an image processing device including receiving first image data of a first type, receiving second image data of a second type, registering the first image data and the second image data by employing a CNN using pixel level registration or object level registration, determining whether a candidate detection of a lesion exists in both the first image data and the second image data based on the registering of the first image data and the second image data, and generating display output identifying the lesion.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/823,972 filed on Mar. 26, 2019, the entire contents of which arehereby incorporated herein by reference.

TECHNICAL FIELD

Example embodiments generally relate to techniques for registration ofimages and, in particular, relate to an apparatus and method foremploying a convolutional neural network (CNN) non-rigid registrationalgorithm for registration of different mammography images.

BACKGROUND

Breast cancer is one of the leading causes of death for women worldwidewith a half million lives lost annually, including 40,000 in the UnitedStates alone. Early detection has been shown to be critical for lessinvasive treatment of breast cancer and for saving lives. Hence, toolsand techniques that can aid clinicians in early detection of breastcancer are invaluable. X-ray based two-view mammography is the mainimaging modality used for breast cancer screening in asymptomatic womenand is also used for more specialized diagnostic exams, which areperformed when suspicious findings or symptoms are present. Conventionalmammography involves two-dimensional (2-D) Full-Field DigitalMammography (FFDM) or, in recent years, Digital Breast Tomosynthesis(DBT). DBT is a relatively new type of digital mammography which was FDAapproved in the United States in 2011.

DBT involves obtaining numerous mammographic images across an arc.Reconstruction generates multiple contiguous 1 mm thick slices throughthe breast, as well as synthesized 2-D images of the entire breast. DBTimages, like FFDM images, are obtained in Craniocaudal (CC) andMediolateral Oblique (MLO) standard mammographic views. Other modalitiessuch as Ultrasound (US), Magnetic Resonance Imaging (MRI), PositronEmission Mammography (PEM) and Molecular Breast Imaging (MBI) can alsobe used to image the breast, but X-ray based mammography is the onlyimaging modality that has been proven to improve outcomes and decreasemortality rates when used as a screening tool.

Mammographic imaging typically involves imaging the breast from at leasttwo different angles. The most frequently used views are the CC and MLOviews mentioned above. The name of each view describes the direction ofthe X-ray beam from the source through the breast to the X-ray detector.Thus, the CC view is obtained at an angle of 0 degrees from the top tothe bottom of the compressed breast and the MLO view is obtained at anangle in the range of 45 to 50 degrees from medial near the center ofthe chest, toward the axilla. Each view involves physically positioningand compressing the breast between two compression plates immediatelyadjacent to an X-ray source and detector.

The purpose of the two views is to include as much breast tissue aspossible, and also to locate lesions by triangulating from theseprojections. Breast lesions may be visible in both views or only on oneview depending on the lesion location in the breast and also dependingon the density of the breast tissue. When breast tissue is very dense,meaning it is made up of mostly fibrous and glandular components, it canobscure lesions, as the background breast tissue will have similar x-rayattenuation compared to a lesion, in essence hiding the finding. This isin contrast to mainly fatty breast tissue where lesions have muchgreater density compared to the fatty tissue, based on the attenuationof the X-ray beam as it travels through breast tissue, making thelesions readily visible.

Currently radiologists analyze images by extrapolating between the twoviews in search of abnormalities. Seeing a lesion on both views is animportant feature, which signals to the radiologist that the lesion ismore likely to be real rather than a false alarm. Additionally, in orderto better characterize breast lesions, visualizing the finding in twoviews is beneficial. Finally, identifying a lesion in both viewslocalizes the finding in the breast, which is critical. Thus, preciseregistration assists clinicians in locating findings, confirmingaccurate lesion detection, and therefore planning further breast imagingevaluation. Registration is essential to guide biopsies and surgicalprocedures as accurate information regarding lesion position isrequired.

Machine learning algorithms and Computer Aided Diagnosis (CAD) processesthat involve joint processing (or fusion) of breast images currentlyexist, and are in development in this area. However, the automatedregistration of mammographic images has proven to be a challenging taskdue to the non-rigid heterogeneous nature of breast tissue and due totissue distortion that can occur as part of breast imaging, includingmammographic compression. Moreover, the resulting pixel-wise mappingsmay not be bijective, but rather involve one-to-many pixel mappings foreach pixel. While advancements in deep learning have generally resultedin numerous improvements in medical image processing, recent surveysindicate that a best approach has not yet been identified for medicalimage registration and that challenges remain in achieving the desiredlevels of accuracy. Thus, it may be desirable to define an improvedautomated registration method for mammographic images.

BRIEF SUMMARY OF SOME EXAMPLES

Some example embodiments may enable the provision of a system that iscapable of providing an improved registration method and device forexecution of the same.

In one example embodiment, a method of identifying potential lesions inmammographic images may include operations executed by an imageprocessing device. The operations may include receiving first image dataof a first type, receiving second image data of a second type,registering the first image data and the second image data by employinga CNN using pixel level registration or object level registration,determining whether a candidate detection of a lesion exists in both thefirst image data and the second image data based on the registering ofthe first image data and the second image data, and generating displayoutput identifying the lesion.

In another example embodiment, a method of identifying potential lesionsin mammographic images via pixel level registration is provided. Themethod may include operations executed by an image processing deviceincluding receiving first image data of a first type, receiving secondimage data of a second type, learning a mapping from a first image ofthe first image data to a second image of the second image data byemploying a CNN, generating a warped image output based on the mapping,determining whether a candidate detection of a lesion exists in both thefirst image data and the second image data based on the warped image,and generating display output illustrating the candidate detection.

In still another example embodiment, a method of identifying potentiallesions in mammographic images via object level registration isprovided. The method may include receiving first image data of a firsttype, receiving second image data of a second type, identifyingcandidate regions by employing a first stage CNN architecture configuredto independently analyze the first image data and the second image datato identify the candidate regions, conducting pairwise evaluation of thecandidate regions to determine whether the candidate detection exists,and determining candidate matches by employing a second stage CNNarchitecture and generating display output identifying the lesion.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 illustrates a functional block diagram of a system foridentifying potential lesions in mammographic images according to anexample embodiment;

FIG. 2 illustrates a functional block diagram of a method foridentifying potential lesions in mammographic images according to anexample embodiment;

FIG. 3A illustrates an architecture for performing the method of FIG. 2using pixel level registration in accordance with an example embodiment;

FIG. 3B illustrates an alternative architecture to that of FIG. 3A,which does not include skip paths, in accordance with an exampleembodiment;

FIG. 4 illustrates an MLO image with a potential lesion in accordancewith an example embodiment;

FIG. 5 illustrates a CC image with a potential lesion in accordance withan example embodiment;

FIG. 6 illustrates a warped image created by pixel level registration ofthe images of FIGS. 4 and 5 in accordance with an example embodiment;

FIG. 7 illustrates the warped image along with an object identificationindicating a potential lesion in accordance with an example embodiment;

FIG. 8 illustrates sample objects in a volume in accordance with anexample embodiment;

FIG. 9 illustrates the sample objects of FIG. 8 moved slightly tofacilitate demonstration of the operation of an example embodiment ofpixel level registration;

FIG. 10 shows a post registration view corresponding to FIGS. 8 and 9 inaccordance with an example embodiment;

FIG. 11 illustrates a before and after view with respect to registrationin accordance with an example embodiment;

FIG. 12 is a quiver plot showing the deformation field in thehighlighted region of FIG. 8 in accordance with an example embodiment;

FIG. 13 illustrates an architecture for performing the method of FIG. 2using object level registration in accordance with an exampleembodiment;

FIG. 14 illustrates a CC image with a potential lesion in accordancewith an example embodiment;

FIG. 15 illustrates the CC image with candidate regions identified inaccordance with an example embodiment;

FIG. 16 illustrates an MLO image with a potential lesion in accordancewith an example embodiment;

FIG. 17 illustrates the MLO image with candidate regions identified inaccordance with an example embodiment;

FIG. 18 illustrates the CC image with a candidate match highlighted inaccordance with an example embodiment;

FIG. 19 illustrates the MLO image with a candidate match highlighted inaccordance with an example embodiment;

FIG. 20 illustrates a plot of lesion distance from nipple for MLO and CCimages in accordance with an example embodiment;

FIG. 21 illustrates a functional block diagram of a method foridentifying potential lesions in mammographic images via pixel levelregistration according to an example embodiment; and

FIG. 22 illustrates a functional block diagram of a method foridentifying potential lesions in mammographic images via object levelregistration according to an example embodiment.

DETAILED DESCRIPTION

Some example embodiments now will be described more fully hereinafterwith reference to the accompanying drawings, in which some, but not allexample embodiments are shown. Indeed, the examples described andpictured herein should not be construed as being limiting as to thescope, applicability or configuration of the present disclosure. Rather,these example embodiments are provided so that this disclosure willsatisfy applicable legal requirements. Like reference numerals refer tolike elements throughout.

As noted above, efforts at image registration between CC and MLO viewshave, to date, not been able to produce consistently satisfying results.In particular, conventional methods have not been able to use both CCand MLO data sets as a collective set from which the same object (orobjects) can be identified in each of the two different views. Exampleembodiments provide a method and apparatus that provide significantlyimproved performance in relation to this endeavor, and actually enabledetection of the same object in each of the two different views. In thisregard, example embodiments provide two different sub-methods forachieving this outcome. One such sub-method employs pixel levelregistration, while the other employs object level registration. Anexample embodiment including apparatuses configured to execute themethod, and the method (which can employ one of the sub-methods) aredescribed in greater detail below.

In this regard, some example embodiments may relate to the provision ofa system that is configured to employ convolutional neural networks(CNNs) to register CC and MLO images in order to find the same objectwithin the images using either pixel level registration or object levelregistration according to the respective sub-methods described herein.The system may be configured to be integrated with imaging equipmentthat obtain images of the same tissue from different views (or differentcollection orientations, including different orientations of thetissue). Thus, for example, the imaging equipment may be configured toprocess common views from breast x-ray imaging including the CC and MLOimages, or supplemental views such as, but not limited to, the ML, LM,LMO. Similarly, the system may be configured to process multiple viewsfrom breast ultrasound imagery, such as, but not limited to radial andanti_radial views. Additionally or alternatively, the system may beconfigured to process multiple views from breast magnetic resonanceimaging (MRI), such as, but not limited to, slices between different MRIsequences such as T1 or T2 sequences, or slices from MRI sequences takenat different times. In all cases, such processing may be done either inreal time or after the fact for offline processing from the imagingequipment and may then process the images accordingly as describedherein. Thereafter, an output may be generated that identifies objectsthat appear in both images (and therefore may represent potentiallesions).

FIG. 1 illustrates a system 10 according to an example embodiment thatmay include a plurality imaging devices (e.g., imager 20). Notably,although FIG. 1 illustrates three imagers 20, it should be appreciatedthat many more imagers 20 may be included in some embodiments and thus,the three imagers 20 of FIG. 1 are simply used to illustrate a potentialfor a multiplicity of imager 20 and the number of imagers 20 is in noway limiting to other example embodiments. Moreover, example embodimentscan also be practiced with fewer imagers 20 and including as little asone imager 20.

The example described herein will be related to an asset comprising aprogrammed computer or analysis terminal (e.g., analysis terminal 30) toillustrate one example embodiment. However, it should be appreciatedthat example embodiments may also apply to any asset including, forexample, any programmable device that is capable of interacting withimage data 40 received from portions of a communication network 50related to image data including CC image data and MLO image datacorresponding to an individual patient. Moreover, the processing of theimage data 40 as described herein could also be performed for multipledifferent patients and include data from the same imager 20 or frommultiple imagers 20 at the same instance of the analysis terminal 30.Thus, one instance of the analysis terminal 30 may handle image data 40from multiple imagers and/or patients. However, it should also beappreciated that the communication network 50 of FIG. 1 could becompletely eliminated and an instance of the imager 20 could beintegrated directly with the analysis terminal 30 in some alternativeembodiments.

Each one of the imagers 20 may be understood to be an x-ray machine orother medical imaging machine that is capable of or otherwise configuredto generate CC and MLO images that form the image data 40. In somecases, the imagers 20 may also or alternatively be configured togenerate other images or views such as medio lateral (ML) and lateralmedial (LM) views. Meanwhile, the analysis terminal 30 may include orotherwise be embodied as computing device (e.g., a computer, a networkaccess terminal, laptop, server, a personal digital assistant (PDA),mobile phone, smart phone, tablet, or the like) capable of beingconfigured to perform data processing as described herein. As such, forexample, the analysis terminal 30 may include (or otherwise have accessto) memory for storing instructions or applications for the performanceof various functions and a corresponding processor for executing storedinstructions or applications. The analysis terminal 30 may also includesoftware and/or corresponding hardware for enabling the performance ofthe respective functions of the analysis terminal 30 including, forexample, the receipt or processing of the image data 40 and thegeneration and/or sharing of various content items including the outputsof the analyses performed on the image data 40 by the analysis terminal30.

The communication network 50 (if employed) may be a data network, suchas a local area network (LAN), a metropolitan area network (MAN), a widearea network (WAN) (e.g., the Internet), and/or the like, which maycouple one or more instances of the imager 20 to devices such asprocessing elements (e.g., personal computers, server computers or thelike) and/or databases. Communication between the communication network50, the imager(s) 20 and the devices or databases (e.g., servers) towhich the imager(s) 20 are coupled may be accomplished by eitherwireline or wireless communication mechanisms and correspondingcommunication protocols. The protocols employed may include security,encryption or other protocols that enable the image data 40 to besecurely transmitted without sacrificing patient privacy.

In an example embodiment, the imager 20 may be coupled via thecommunication network 50 to an image registration module 60. The imageregistration module 60 may be operably coupled to a user interface 70 toform respective portions of the analysis terminal 30. An operator 80 maybe enabled to interface with the analysis terminal 30 via the userinterface 70 to operate the image registration module 60 in order toreceive object registration data 90 as described in greater detailbelow.

The analysis terminal 30 of FIG. 1 may represent an apparatus forprovision of the image registration capabilities described hereinaccording to an example embodiment. The analysis terminal 30 may beemployed, for example, on a device such as, for example, a computer, anetwork device, server, proxy, or the like at which the imageregistration module 60 may be instantiated. It should be noted that thedevices or elements described below may not be mandatory and thus somemay be omitted in certain embodiments.

Referring still to FIG. 1, an apparatus for provision of imageregistration between CC and MLO images of the image data 40 inaccordance with an example embodiment is provided. However, it should beappreciated that the apparatus may also be capable of findingcorrespondence between either of these views and LM or ML views as well.Thus, the application specifically to CC and MLO views described hereinshould be appreciated as being a non-limiting example. The apparatus maybe an embodiment of the image registration module 60. As such,configuration of the apparatus as described herein may transform theapparatus into the image registration module 60. In an exampleembodiment, the apparatus may include or otherwise be in communicationwith processing circuitry 100 that is configured to perform dataprocessing, application execution and other processing and managementservices according to an example embodiment of the present invention. Inone embodiment, the processing circuitry 100, which may include aprocessor 102 and a storage device 104, may be in communication with orotherwise control the user interface 70 and the image registrationmodule 60. As such, the processing circuitry 100 may be embodied as acircuit chip (e.g., an integrated circuit chip) configured (e.g., withhardware, software or a combination of hardware and software) to performoperations described herein. However, in some embodiments, theprocessing circuitry 100 may be embodied as a portion of a server,computer, laptop, workstation or even one of various mobile computingdevices. In situations where the processing circuitry 100 is embodied asa server or at a remotely located computing device, the user interface70 may be disposed at another device that may be in communication withthe processing circuitry 100 via a network (e.g., communication network50).

The user interface 70 may be in communication with the processingcircuitry 100 to receive an indication of a user input at the userinterface 70 and/or to provide an audible, visual, mechanical or otheroutput to the user (e.g., image registration data 90). As such, the userinterface 70 may include, for example, a keyboard, a mouse, a joystick,a display, a touch screen, a microphone, a speaker, a cell phone, orother input/output mechanisms. In embodiments where the apparatus isembodied at a server or other network entity, the user interface 70 maybe limited or even eliminated in some cases. Alternatively, as indicatedabove, the user interface 70 may be remotely located. In some cases, theuser interface 70 may also include a series of web pages or interfaceconsoles generated to guide the user through various options, commands,flow paths and/or the like for control of or interaction with the imageregistration module 60. The user interface 70 may also include interfaceconsoles or message generation capabilities to send instructions,warnings, alerts, etc., and/or to provide an output that clearlyindicates a correlation between objects in the different types of images(e.g., the CC images and the MLO images) of the image data 40.

In an example embodiment, the storage device 104 may include one or morenon-transitory storage or memory devices such as, for example, volatileand/or non-volatile memory that may be either fixed or removable. Thestorage device 104 may be configured to store information, data,applications, instructions or the like for enabling the apparatus tocarry out various functions in accordance with example embodiments ofthe present invention. For example, the storage device 104 could beconfigured to buffer input data for processing by the processor 102.Additionally or alternatively, the storage device 104 could beconfigured to store instructions for execution by the processor 102. Asyet another option, the storage device 104 may include one of aplurality of databases that may store a variety of files, contents ordata sets, or structures used to embody one or more of the CNNsdescribed herein. Among the contents of the storage device 104,applications may be stored for execution by the processor 102 in orderto carry out the functionality associated with each respectiveapplication.

The processor 102 may be embodied in a number of different ways. Forexample, the processor 102 may be embodied as various processing meanssuch as a microprocessor or other processing element, a coprocessor, acontroller or various other computing or processing devices includingintegrated circuits such as, for example, an ASIC (application specificintegrated circuit), an FPGA (field programmable gate array), a hardwareaccelerator, or the like. In an example embodiment, the processor 102may be configured to execute instructions stored in the storage device104 or otherwise accessible to the processor 102. As such, whetherconfigured by hardware or software methods, or by a combination thereof,the processor 102 may represent an entity (e.g., physically embodied incircuitry) capable of performing operations according to embodiments ofthe present invention while configured accordingly. Thus, for example,when the processor 102 is embodied as an ASIC, FPGA or the like, theprocessor 102 may be specifically configured hardware for conducting theoperations described herein. Alternatively, as another example, when theprocessor 102 is embodied as an executor of software instructions, theinstructions may specifically configure the processor 102 to perform theoperations described herein.

In an example embodiment, the processor 102 (or the processing circuitry100) may be embodied as, include or otherwise control the imageregistration module 60, which may be any means such as a device orcircuitry operating in accordance with software or otherwise embodied inhardware or a combination of hardware and software (e.g., processor 102operating under software control, the processor 102 embodied as an ASICor FPGA specifically configured to perform the operations describedherein, or a combination thereof) thereby configuring the device orcircuitry to perform the corresponding functions of the imageregistration module 60 as described herein.

FIG. 2 illustrates a block diagram showing a high level functioning ofthe image registration module 60 of an example embodiment. In thisregard, the image registration module 60 may be configured to receivethe image data 40, which may include CC images and MLO images atoperation 200. The image registration module 60 may then be configuredto apply the image data 40 to one or more CNNs, where the one or moreCNNs are configured to perform either pixel level registration or objectlevel registration-based sub-methods at operation 210. The imageregistration module 60 may generate object registration data 90 atoperation 220 as a result of the application of the one or more CNNs tothe image data 40 in operation 210. The object registration data 90 mayinclude an indication of a detection of the same thing (i.e., the sameobject or anomaly) in the CC images and MLO images of the image data 40.

Of note, the image registration module 60 may be configured to performonly one of the pixel level registration-based sub-method or the objectlevel registration-based sub-method in some cases. However, in otherembodiments, the image registration module 60 may be configured toperform both the pixel level registration-based sub-method and theobject level registration-based sub-method. In such an example, theoperator 80 may use the user interface 70 to select which one of thepixel level registration-based sub-method or the object levelregistration-based sub-method should be used for a given set of theimage data 40. As yet another alternative, the operator 80 may select anoption to run both the pixel level registration-based sub-method and theobject level registration-based sub-method (an any desired order, or inparallel) to check agreement or potential differences in the objectregistration data 90 generated by each respective sub-method. Theoperator may also interface with the object registration data 90 toselect regions of interest or potential lesions or to otherwise driveoperation of the image registration module 60 in the manner describedherein.

Structures and operations associated with each of the pixel levelregistration-based sub-method and the object level registration-basedsub-method will now be described in reference to FIGS. 3-20. In thisregard, FIGS. 3-12 will be used to describe pixel level registration andFIGS. 13-20 will be used to describe object level registration inaccordance with respective example embodiments. Both sub-methods employCNNs, which are known by those having skill in the art to include inputand output layers with potential for hidden layers or skip paths, andemploy convolution (a special kind of linear operation) instead ofgeneral matrix multiplication in at least one of the layers. Theactivation function of a CNN is commonly a rectified linear unit (ReLu)layer, and may be followed by additional convolutions or pooling layers,and a final convolution, among other things. Convolutional layers willconvolve the input and pass a result to the next layer. For images,passing through a convolution layer generally abstracts the image to afeature map. Pooling layers tend to reduce the dimensions of data bycombining outputs prior to processing at a next layer.

FIG. 3A illustrates an architecture for performing pixel levelregistration (i.e., executing the pixel level registration-basedsub-method) of a first image 300 (i.e., image 1 or I₁) and a secondimage 302 (i.e., image 2 or I₂) in accordance with an exampleembodiment. In particular, the architecture of FIG. 3A employs adeformation-field based CNN network for registering mammographic images.As shown in FIG. 3A, the first image 300 may be a CC image and thesecond image 302 may be an MLO image. However, either or both of thesecould be replaced with ML or LM views in various other alternativeexamples. Various image processing steps will be described in relationto the first and second images 300 and 302 based on the architectureshown in FIG. 3A. However, it should be appreciated that the first andsecond images 300 and 302 could be swapped with respect to theprocessing described without changing results thereof. In other words,if the MLO image were instead the first image 300 and the CC image wereinstead the second image 302 the processing described and resultsachieved would not change. Moreover, it should also be appreciated thatexample embodiments may be practiced on multiple instances of each ofthe first and second images 300 and 302 even though only two specificimages are shown in this example.

The first and second images 300 and 302 may each be fed into a CNN 310.The input images are pre-processed using custom techniques (some ofwhich are optional). The following are examples. First, as is typicalfor CNN's, the input images may be resampled to a fixed number of rowand column pixels (e.g., 3000×2000). Next, the images may be flipped, ifneeded, about a vertical axis so that the breast scene is alwaysoriented in a certain direction (e.g., chest-wall or pectoral muscle onthe left, and nipple towards the right.) Additionally, image processingmay be applied to generate a “mask-image”, which is an image raster thesame size as the input image, but which only has two gray levels: aforeground gray level representing the breast tissue, and a backgroundvalue representing the rest of the image. Further, custom imageprocessing algorithms for detecting the location of the breast nipple ineach image may be applied. This location information may be used forsupporting parts of the loss function 350 during training. Also,exclusive to the MLO input image, custom image processing may be appliedfor detecting and masking the pectoral muscle in the image. Other,custom image processing may also be applied. It is noted that theminimal required processing above is the input image re-sizing and theimage flipping (or the ensuring that the breast scenes are oriented in acertain direction.) The CNN 310 may be a fully convolutional network(FCN) and is not a fully connected CNN, nor does it have fully connectedcomponents as some CNN's do. An example of such an FCN is described inJ. Long, E. Shelhamer and T. Darrell, “Fully convolutional networks forsemantic segmentation,” 2015 IEEE Conference on Computer Vision andPattern Recognition (CVPR), Boston, Mass., 2015, pp. 3431-3440, theentire contents of which are hereby incorporated herein by reference.Another example of an FCN is described in H. Li and Y. Fan, “Non-rigidimage registration using fully convolutional networks with deepself-supervision,” the Department of Radiology, Perelman School ofMedicine, University of Pennsylvania, Sep. 4, 2017, the entire contentsof which are hereby also incorporated herein by reference. A benefit ofthe FCN not being fully connected is that the CNN's can ingest an inputimage of arbitrary size and produce an output raster at that same size.On the contrary, with both Fully Connected Networks and even CNNs thatuse fully connected components in their final layers, the size of theoutput raster is more so constrained based on the number of outputnodes. With an FCN, the same network could be used to process inputimages of different sizes. (However, in some examples, in the trainingphase, the network parameters—such as convolution kernel sizes—may bedynamically set based on the size of the input images.) The outputproducts—the warped images and deformation fields—would also have thesame size as the inputs. This is not typically true for the other twocases of networks, for which the input and output image sizes tend to beconstrained based on the number of input and output nodes (or neurons)and the transfer functions that are used at the output. The CNN 310 mayinclude a skip architecture that includes a first skip path 304 and asecond skip path 306 in addition to the main serial path, which extendsfully through the CNN 310 from left to right in the image shown in FIGS.3A and 3B. However, it should be appreciated that an alternativeconfiguration could be designed without the first and second skip paths304 and 306 (and therefore using only the main serial path) as shown byFIG. 3B. The CNN 310 does not include any fully connected layer.Additionally, the convolution layers (e.g., 311, 312, 313, 314 and 319)of the example of FIG. 3A includes ReLu and batch normalization (exceptfor the final convolution layers along each path, for which feature mapsserve as optimized deformation field components).

The function of the CNN 310 is to learn and generate a non-parametricdeformation field, which can be used to map pixels from the first image300 to pixels of corresponding image components in the second image 302to identify objects of interest. In other words, the CNN 310 isconfigured to map pixels from one image to pixels for the same imagecomponents in another image, especially pixels that pertain to certainanomalous tissue (i.e., lesions). As such, with respect to the firstimage 300 and the second image 302, the CNN 310 learns a mapping (ordeformation field) between the first and second images 300 and 302.

As noted above, the input to the CNN 310 may be a concatenated pair ofmammographic images with dimensionsM×N×2. One image is a Craniocaudalimage (CC image) (e.g., a two-dimensional Craniocaudal x-raymammographic image) (i.e., the first image 300) and the other is aMediolateral Oblique image (MLO image) (e.g., a two-dimensionalMediolateral x-ray image) (i.e., the second image 302). In the top,serial, path of the CNN 310, four convolution layers (conv. 1—311, conv.2—312, conv. 3—313, conv. 4—314) each operate to generate down sampledfeature maps. The first two convolutional layers (i.e., conv. 1—311 andconv. 2—312) are followed by respective pooling layers (e.g., pool 1—315and pool 2—316) that aggregate the features mapped in the convolutionallayers. The use of pooling layers also reduces resolution to lowermemory requirements for the system. In an example embodiment, conv.1-311 may have a 4×4 kernel and an output matrix of 1500×1000 pixels.Pool 1-315 may have a 2×2 stride and an output matrix of about 750×500pixels. Cony. 2—312 may have a 4×4 kernel and an output of 375×250pixels while pool 2—316 has a 3×2 stride and output of 187×125 pixels.The third and fourth convolutional layers (conv. 3—313 and conv. 4-314)are followed by two deconvolution layers (deconv. 1—317 and deconv.2—318), respectively, which up-sample the feature maps back to the sameresolution as the resolution of input images (e.g., the first and secondimages 300 and 302). As such, conv. 3—313 may have a 3×3 kernel andoutput of 187×125 pixels, deconv. 1—317 may have a 6×6 kernel and anoutput of 750×125 pixels, conv. 4—314 may have a 3×3 kernel and outputof 750×125 pixels, and deconv. 2—318 may have a 6×6 kernel and output of3000×2000 pixels. A final convolution layer 319 involves two channels(321 and 322), which provide row and column deformation components 323(e.g., vectors) for the skip paths, and outputs to a deformation field(e.g., deformation field C 334). The final convolutional layer 319 mayhave a 1×1 kernel and output of 3000×2000 pixels. The vectors or row andcolumn deformation components 323 are applied to the second image 302(e.g., the MLO image) to move pixels around so that the resulting image(e.g., warped image) look like the first image 300 (e.g., the CC image).This will cause overlap of features and potentially, if a featurecorresponds to a lesion, effectively cause the lesion to remainprominent in the warped image. In some cases, the operator can select orclick on a feature (e.g., a potential lesion) and alternate throughother views (e.g., the CC image or the MLO image) to see the samefeature in the other view.

The two skip paths (e.g., the first and second skip paths 304 and 306)each include a deconvolution stage (e.g., stages 342 and 340,respectively) and a convolution stage (e.g., stages 346 and 344,respectively). The skip paths also generate additional deformationfields (e.g., deformation field A 330 and deformation field B 332) basedon information at earlier stages in the network. The resultingdeformations from each of the paths (e.g., deformation field A 330,deformation field B 332, and deformation field C 334) may be up-sampledto the same resolution as the input images (e.g., the first and secondimages 300 and 302—3000×2000 pixels in this example), averaged, and usedto warp desired regions of one of the input channels to correspondingregions of the other input channel by loss function 350.

The deformation field involves two, two-dimensional arrays, onecontaining vertical (row-wise) deformation components and one containinghorizontal (column-wise) deformation components. Together, theseconstitute a two-dimensional deformation vector. (The general networkarchitecture could also be extended to generate a three-dimensionaldeformation field.) Convolution layers 319, 344 and 346 in FIG. 3A, and319 in FIG. 3B generate the two arrays (also known as feature maps).Modules associated with generating deformation fields (e.g., 334, 332,and 330), take the deformation components and use them to shift eachpixel in the MLO input image to the location constituted by thecorresponding deformation vector. The deformation equation isessentially, as shown below, where x and y are row an column coordinatesfor a specific pixel in the input MLO image. The x coordinate istranslated by a vector, u, that is formed from values that are at thesame array position in the vertical and horizontal deformation componentarrays. The y coordinate is translated in similar fashion using vector,v.

Im _(def)(x,y)=Im(x+u(x,y),y+v(x,y))

-   -   (Note: u and v are rounded to serve as indices)

As noted earlier, during training, the network layer parameters, such asconvolutional and pooling kernel sizes are set dynamically based on thesize of the input images.

Optimization may be performed using the loss function 350 involving asimilarity metric and one or more regularization techniques. One exampleof the loss function 350 is shown below.

-   -   Example Loss Function (for one path of the network).    -   Loss=Σ_(i=1) ^(L)|Y_(i)−T_(i)|+λΣ_(i=1) ^(L)∥∇d(i)∥₁, where, Y        is the resulting warped image by the network, T, is the target        image, d, is the deformation field, and L is the number of        pixels in the image. (The loss from each network path is        weighted and summed.)

Ln=S(I ₁(x),I ₂(D _(n)(x))+λR(D _(nx)), where n=A,B, or C.

The output of the CNN 310 may include a warped image 360 based on theapplication of the deformation fields to the respective input image(e.g., second image 302) which needs to be registered to the targetimage (e.g., first image 300), and a deformation field 365 showingvector movement of pixels between the first and second images 300 and302. Object identification 370 may then be performed to indicate anobject that is common to both the input image and the target image byusing the deformation field to provide a functionality such as allowinga user to click on the original input MLO image and having thesystem—using the deformation field for a mapping—to show thecorresponding tissue on the CC image. The provision of the deformationfield 365 may also support implementation of other functions forgenerating visualizations related to determining correspondences betweenfeatures in the first and second images 300 and 302 (e.g., from originalpositions in one image to final positions in the other). Further, thedeformation field output may support other machine learning or imageprocessing systems by providing a means (i.e., the deformation-basedmapping) for relating pixel or object detections in one image to theother.

FIGS. 4-7 show an example of images (e.g., using de-identified data) andresults that may be involved in the operation of the CNN 310 of FIG. 3A.In this regard, FIG. 4 shows an MLO image 400 with a candidate object410 visible therein. FIG. 5 shows a CC image 420 with a candidate object430 visible therein. Either of the MLO image 400 or the CC image 420could act as the input image or the target image. In either case, one ofthe images will be warped to the other. In the example of FIG. 6, theMLO image 400 has been warped to the CC image 420 to generate warpedimage 440. Although not required, masks or other image processingtechniques could be used to address background or burned-in annotations.However, in this case, candidate combined object 450, which is visiblein the warped image 440, appears to correlate to both the candidateobject 410 and candidate object 430 without any need for additionalprocessing. As such, the object identification 470 may be added to thewarped image 440 to highlight the candidate combined object 450 to apractitioner (e.g., a radiologist) for evaluation.

Performance of the architecture of FIG. 3A has also been tested onsurrogate three dimensional (3-D) Mixed National Institute of Standardsand Technology (MNIST) letters and is demonstrated in relation to FIGS.8-12. In this regard, FIG. 8 illustrates a first image 500 (e.g., a 2-Dimage) of three letters, two of which are located in a highlightedregion 502, randomly placed in a 3-D space (e.g., a cylinder). Theletter orientations or positions may then be slightly altered tosimulate some level of non-rigidity of the objects and then the 3-Dspace is rotated about the x-axis about 45 degrees (e.g., 40-50degrees). The second image 504 of FIG. 9 is then captured. If the firstimage 500 is considered to be an input image (i.e., the image toregister), and the second image 504 is considered the target image,deformation fields generated by the CNN 310 of FIG. 3A may serve totranslate pixels from the input image to the corresponding locations inthe target image (thereby warping the input image to the translatedimage). A registration result 506 is shown in FIG. 10. Meanwhile, FIG.11 shows a before and after view 508 for before and after registration.

The projections associated with FIGS. 8-11 can serve as two-channelinputs to the CNN 310 of FIG. 3A. Each of the images of FIGS. 8-11 areimages of 1024×1024 pixels to demonstrate performance in ahigh-resolution environment. However, testing with lower resolutionperformance (e.g., 128×128 pixels) is also possible. In an exampleembodiment, 9000 random projection pairs were generated (therefore18,000 total images) and 90% of the projection pairs were used fortraining, while 10% were used for testing. For the simplistic modelingassociated with FIGS. 8-11, the test results clearly demonstrate thehigh degree to which example embodiments are able to warp an input image(or moving image) to a target image (or fixed image). FIG. 12 shows aquiver plot 510 of deformation field vectors for the highlighted region502 of FIG. 8. Thus, FIG. 12 shows pixel level registration of theletter projections in the highlighted region 502 to illustrate how thepixels for the letters A and B moved from FIG. 8 to FIG. 9. Withadditional training data, the possibility of refining networkperformance even further may also exist. However, the examples of FIGS.4-12 clearly demonstrate the ability of the image registration module 60to employ the first sub-method (i.e., pixel level registration with CNNdeformation field learning) to effectively perform registration betweenCC and MLO image data, and to identify the same objects appearing inboth.

As noted above, the image registration module 60 may also employ thesecond sub-method (i.e., object level registration). FIG. 13 illustratesan architecture that can be used to support object level registration inaccordance with an example embodiment. The architecture of FIG. 13employs a CNN architecture known as region-based CNN (R-CNN) The R-CNNarchitecture may be employed based on the descriptions by R. Girshick,J. Donahue, T. Darrell, and J. Malik in “Rich Feature Hierarchies forAccurate Object Detection and Semantic Segmentation,” from CVPR '14Proceedings of the 2014 IEEE Conference on Computer Vision and PatternRecognition at pages 580-587, 2014, the contents of which are herebyincorporated herein by reference. In this regard, FIG. 13 illustrates atwo-stage, dual R-CNN based architecture in which the first stageincludes two R-CNNs that process respective image sets to find candidateobjects isolated to the respective images sets themselves. Then thesecond stage includes another CNN that is configured to process outputpairs from the first stage.

As shown in FIG. 13, a first set of images 700 may include a pluralityof images of a first image type (e.g., CC images), and a second set ofimages 710 may include a plurality of images of a second image type(e.g., MLO images). The first set of images 700 may be fed into a firstR-CNN 720 and the second set of images 710 may be separately fed into asecond R-CNN 722. The first and second R-CNNs 720 and 722 aredisconnected, and do not have any connection therebetween. Additionally,whereas a conventional CNN processes an entire image, the first andsecond R-CNNs 720 and 722 may be configured to first identify one ormore smaller candidate regions within an image and process only thosesmaller candidate regions. The processing of smaller regions means thatless memory is required for the processing. The candidate regions arenot fixed regions, but may be regions selected based on a regionproposal function associated with the CNN. Accordingly, the first andsecond R-CNNs 720 and 722 may require less memory for processing, andcan therefore be expected to handle larger images. The ability to handlelarger images is important for medical image processing since it isimportant to maintain the original pixel resolution of source images topreserve subtle differences in texture.

When the first set of images 700 is received by the first R-CNN 720, thefirst R-CNN 720 operates to generate a series of first candidate regions730 based on analysis of the first set of images 700. The firstcandidate regions 730 may each identify regions that include potentiallesions based on a scoring value generated by the first R-CNN 720.Similarly, the second R-CNN 722 operates to generate a series of secondcandidate regions 732 based on analysis of the second set of images 710.The second candidate regions 732 are also identified regions that mayinclude lesions based on a scoring value generated by the second R-CNN722. A pairwise candidate combination evaluation 740 is then performed,potentially over a number of different combinations of pairs. Thescoring values may be used filter out certain combinations of pairs, andremaining pairs (i.e., those not filtered out, and therefore havingscoring values above a threshold value) may be merged into atwo-image-layer product. However, a third layer or band may be generatedprior to final processing by final stage CNN 750. The third band may begenerated based on an absolute difference between relativedistances-from-nipple for each potential detection. In this regard, αshows a distance from a potential detection 760 and a nipple 762 for aCC image, and β shows a distance from a potential detection 764 and thenipple 762. Equation 766 shows how γ may then be calculated based on theabsolute difference between a and β. γ may be provided as a layer in thesecond stage CNN. The CNN may use the γ and, based on a statisticalcorrelation between lesions in CC and MLO images, γ may facilitatefinding image patches that go together via the CNN. Patches that aresignificantly different distances from the nipple do not likely belongtogether since a lesion should have the same distance from the nipple inboth CC and MLO views. FIG. 20 shows a graph 790 of lesion distance fromnipple in MLO images to the lesion distance from nipple in CC images. Inthis regard, FIG. 20 shows a linear correlation in the relativelesion-to-nipple distance for the same lesions in both views.

Accordingly, a three-image-layer input is provided to the final stageCNN 750, and the final stage CNN 750 operates to classify each set ofpair candidates (between CC and MLO images) as either matching ornot-matching based on a lower γ indicating a greater likelihood of amatch. Candidate matches 770 may therefore be generated from thecandidate pairs that are classified as matching. The candidate matches770 may therefore be understood to illustrate detections of the sameobject (e.g., a potential lesion) in both of the different types ofimages (CC and MLO images).

An example is shown in FIGS. 14-19 to demonstrate the operation of thesub-method shown in FIG. 13. In this regard, FIG. 14 illustrates a CCimage 800 that may be one of the first set of images 700 of FIG. 13.FIG. 15 illustrates a first candidate region 802, a second candidateregion 804 and a third candidate region 806 that may be generated as thefirst candidate regions 730 of FIG. 13 due to operation of the firstR-CNN 720. FIG. 16 illustrates an MLO image 810 that may be one of thesecond set of images 710 of FIG. 13. FIG. 17 illustrates a fourthcandidate region 812, and a fifth candidate region 814 that may begenerated as the second candidate regions 732 of FIG. 13 due tooperation of the second R-CNN 722.

The pairwise candidate combination evaluation 740 may then be performedalong with processing by the final stage CNN 750 to produce candidatematches 770 shown in FIGS. 18 and 19. In this regard, FIG. 18 shows theCC image 800, with candidate match 820 highlighted thereon. Similarly,FIG. 19 shows the MLO image 810 with candidate match 822 highlightedthereon. The candidate matches 820 and 822 may enable a practitioner(e.g., radiologist) to quickly and easily evaluate the CC and MLO imagedata.

Based on the descriptions above, it can be appreciated that the methodof FIG. 2 defines a general methodology for detecting potential lesionsin two different types of images (e.g., CC and MLO images). The generalmethodology may include either (or both) of two individual sub-methodsthat have been separately described above. FIG. 21 illustrates a blockdiagram of one of those sub-methods (i.e., using pixel levelregistration) and FIG. 22 illustrates a block diagram of the other ofthose sub-methods (i.e., using object level registration).

Referring now to FIG. 21, a method of identifying potential lesions inmammographic images via pixel level registration, which can be executedby an image processing device is provided. The method may includereceiving first image data of a first type at operation 900, andreceiving second image data of a second type at operation 910. Themethod may further include employing a CNN to learn a mapping from afirst image of the first image data to a second image of the secondimage data at operation 920, and generating a warped image output basedon the mapping at operation 930. The method may also include determiningwhether a candidate detection of a lesion exists in both the first imagedata and the second image data based on the warped image at operation940, and generating a display output identifying the lesion (e.g., anobject identification illustrating the candidate detection) at operation950.

In some embodiments, the features or operations described above may beaugmented or modified, or additional features or operations may beadded. These augmentations, modifications and additions may be optionaland may be provided in any combination. Thus, although some examplemodifications, augmentations and additions are listed below, it shouldbe appreciated that any of the modifications, augmentations andadditions could be implemented individually or in combination with oneor more, or even all of the other modifications, augmentations andadditions that are listed. As such, for example, the method may furtherinclude application of a skip architecture within the CNN. The skiparchitecture may include one or more skip paths, and each of the one ormore skip paths may generate a corresponding deformation field. In somecases, the first image data is two-dimensional CC mammographic imagedata and the second image data is two-dimensional MLO mammographic imagedata. In an example embodiment, determining whether the candidatedetection exists in both the first image data and the second image datamay include analyzing the warped image output for correlated featuresassociated with the candidate detection, and generating the objectidentification may include generating the object identification on thewarped image. In some cases, the CNN may be a fully convolutionalnetwork. Alternatively or additionally, the CNN may be configured sothat it does not include any fully connected layer. Also, the firstimage data and the second image data may be input to the CNN in separatechannels such that an output of the CNN comprises two channels definingrow and column deformation components.

Referring now to FIG. 22, a method of identifying potential lesions inmammographic images via object level registration is provided. Themethod may be executed by an image processing device and may includereceiving first image data of a first type at operation 1000, receivingsecond image data of a second type at operation 1010, and identifyingcandidate regions by employing a first stage CNN architecture configuredto independently analyze the first image data and the second image datato identify the candidate regions at operation 1020. The method mayfurther include conducting pairwise evaluation of the candidate regionsto determine whether the candidate detection exists at operation 1030,and determining candidate matches by employing a second stage CNNarchitecture and generating display output identifying the lesion atoperation 1040.

In some embodiments, the features or operations described above may beaugmented or modified, or additional features or operations may beadded. These augmentations, modifications and additions may be optionaland may be provided in any combination. Thus, although some examplemodifications, augmentations and additions are listed below, it shouldbe appreciated that any of the modifications, augmentations andadditions could be implemented individually or in combination with oneor more, or even all of the other modifications, augmentations andadditions that are listed. As such, for example, the first image datamay be two-dimensional CC mammographic image data and the second imagedata is two-dimensional MLO mammographic image data. In an exampleembodiment, employing the first stage CNN architecture may includeproviding a plurality of first images associated with the first imagedata to a first R-CNN trained to identify first candidate regions, andproviding a plurality of second images associated with the second imagedata to a second R-CNN trained to identify second candidate regions. Thefirst and second candidate regions may include the candidate regions onwhich the pairwise evaluation is conducted. In an example embodiment,employing the second stage CNN architecture may include providing thesecond stage CNN architecture with data associated with the firstcandidate regions and the second candidate regions, and distance fromnipple information for each instance of the candidate detection in thedata associated with the first candidate regions and the secondcandidate regions. In some cases, generating the object identificationmay include generating the object identification on both the firstimages and the second images.

From a technical perspective, the image registration module 60 describedabove may be used to support some or all of the operations describedabove. As such, the platform described in FIG. 1 may be used tofacilitate the implementation of several computer program and/or networkcommunication based interactions. As an example, FIGS. 2, 21 and 22 areeach examples of a flowchart of a method and program product accordingto an example embodiment of the invention. It will be understood thateach block of the flowchart, and combinations of blocks in theflowchart, may be implemented by various means, such as hardware,firmware, processor, circuitry and/or other device associated withexecution of software including one or more computer programinstructions. For example, one or more of the procedures described abovemay be embodied by computer program instructions. In this regard, thecomputer program instructions which embody the procedures describedabove may be stored by a memory device of a user terminal and executedby a processor in the user terminal. As will be appreciated, any suchcomputer program instructions may be loaded onto a computer or otherprogrammable apparatus (e.g., hardware) to produce a machine, such thatthe instructions which execute on the computer or other programmableapparatus create means for implementing the functions specified in theflowchart block(s). These computer program instructions may also bestored in a computer-readable memory that may direct a computer or otherprogrammable apparatus to function in a particular manner, such that theinstructions stored in the computer-readable memory produce an articleof manufacture which implements the functions specified in the flowchartblock(s). The computer program instructions may also be loaded onto acomputer or other programmable apparatus to cause a series of operationsto be performed on the computer or other programmable apparatus toproduce a computer-implemented process such that the instructions whichexecute on the computer or other programmable apparatus implement thefunctions specified in the flowchart block(s).

Accordingly, blocks of the flowchart support combinations of means forperforming the specified functions and combinations of operations forperforming the specified functions. It will also be understood that oneor more blocks of the flowchart, and combinations of blocks in theflowchart, can be implemented by special purpose hardware-based computersystems which perform the specified functions, or combinations ofspecial purpose hardware and computer instructions.

In an example embodiment, an apparatus for performing the method of FIG.2, 21 or 22 above may comprise a processor (e.g., the processor 102) orprocessing circuitry configured to perform some or each of theoperations (200-220, 900-950 and 1000-1040) described above. Theprocessor may, for example, be configured to perform the operations(200-220, 900-950 and 1000-1040) by performing hardware implementedlogical functions, executing stored instructions, or executingalgorithms for performing each of the operations. In some embodiments,the processor or processing circuitry may be further configured toperform the additional operations or optional modifications tooperations 200-220, 900-950 and 1000-1040 that are discussed above.

Example embodiments provide a fully convolutional (versus fullyconnected) CNN that is non-rigid deformation-field based. In thisregard, since breast tissue is non-rigid and heterogeneous in nature,such a characteristic of the network is important to successfulregistration within this context. The environment or context in whichexample embodiments operate is particularly challenging since the breasttissue is compressed and repositioned in the different views used, andsince the viewing angles employed between the views used are alsodifferent. The CNN employed by example embodiments can thereforeregister images to find correspondences between images (e.g., salientfeatures, which may be bright features in the CC or MLO images) that aredifferent in these important ways. Example embodiments may also becapable of operating at the pixel level within this challenging context.Example embodiments are also distinct from other architectures (e.g.,Siamese architectures) since these architectures share network weightsbetween paths. Meanwhile, example embodiments do not share weightsbetween paths so that each path is unique and has its own weights, andcan learn independently of the other path.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, although the foregoing descriptions and the associateddrawings describe exemplary embodiments in the context of certainexemplary combinations of elements and/or functions, it should beappreciated that different combinations of elements and/or functions maybe provided by alternative embodiments without departing from the scopeof the appended claims. In this regard, for example, differentcombinations of elements and/or functions than those explicitlydescribed above are also contemplated as may be set forth in some of theappended claims. In cases where advantages, benefits or solutions toproblems are described herein, it should be appreciated that suchadvantages, benefits and/or solutions may be applicable to some exampleembodiments, but not necessarily all example embodiments. Thus, anyadvantages, benefits or solutions described herein should not be thoughtof as being critical, required or essential to all embodiments or tothat which is claimed herein. Although specific terms are employedherein, they are used in a generic and descriptive sense only and notfor purposes of limitation.

That which is claimed:
 1. A method of identifying potential lesions inmammographic images, the method comprising: receiving, by an imageprocessing device, first image data of a first type; receiving, by theimage processing device, second image data of a second type;registering, by the image processing device, the first image data andthe second image data by employing a convolutional neural network (CNN)using pixel level registration or object level registration;determining, by the image processing device, whether a candidatedetection of a lesion exists in both the first image data and the secondimage data based on the registering of the first image data and thesecond image data; and generating, by the image processing device,display output identifying the lesion.
 2. The method of claim 1, whereinthe first image data is two-dimensional Craniocaudal (CC) mammographicimage data and the second image data is two-dimensional MediolateralOblique (MLO) mammographic image data.
 3. The method of claim 2, whereinthe pixel level registration comprises: learning a mapping from a firstimage of the first image data to a second image of the second image datavia the CNN; and generating a warped image output based on the mapping.4. The method of claim 3, wherein determining whether the candidatedetection exists in both the first image data and the second image datacomprises analyzing the warped image output for correlated featuresassociated with the candidate detection, and wherein generating thedisplay output comprises generating an object identification on thewarped image.
 5. The method of claim 3, wherein the CNN is a fullyconvolutional network, wherein the CNN does not include any fullyconnected layer, and wherein the first image data and the second imagedata are input to the CNN in separate channels such that an output ofthe CNN comprises two channels defining row and column deformationcomponents.
 6. The method of claim 3, wherein the CNN comprises a skiparchitecture including one or more skip paths, and wherein each of theone or more skip paths generates a corresponding deformation field. 7.The method of claim 2, wherein the object level registration comprises:employing a first stage CNN architecture configured to independentlyanalyze the first image data and the second image data to identifycandidate regions; conducting pairwise evaluation of the candidateregions to determine whether the candidate detection exists; andemploying a second stage CNN architecture configured to determinecandidate matches and generate object identification based on thepairwise evaluation.
 8. The method of claim 7, wherein employing thefirst stage CNN architecture comprises: providing a plurality of firstimages associated with the first image data to a first region-based CNN(R-CNN) trained to identify first candidate regions; and providing aplurality of second images associated with the second image data to asecond R-CNN trained to identify second candidate regions, wherein thefirst and second candidate regions comprise the candidate regions onwhich the pairwise evaluation is conducted.
 9. The method of claim 8,wherein employing the second stage CNN architecture comprises providingthe CNN with data associated with the first candidate regions and thesecond candidate regions, and distance from nipple information for eachinstance of the candidate detection in the data associated with thefirst candidate regions and the second candidate regions.
 10. The methodof claim 9, wherein generating the display output comprises generatingan object identification on both the first images and the second images.11. The method of claim 2, further comprising utilizing both the pixellevel registration to generate a first object identification and theobject level registration to generate a second object identification,and comparing the first and second object identifications.
 12. A methodof identifying potential lesions in mammographic images, the methodcomprising: receiving, by an image processing device, first image dataof a first type; receiving, by the image processing device, second imagedata of a second type; learning, by the image processing device, amapping from a first image of the first image data to a second image ofthe second image data by employing a convolutional neural network (CNN);generating, by the image processing device, a warped image output basedon the mapping; determining, by the image processing device, whether acandidate detection of a lesion exists in both the first image data andthe second image data based on the warped image; and generating, by theimage processing device, display output identifying the lesion.
 13. Themethod of claim 12, wherein the first image data is two-dimensionalCraniocaudal (CC) mammographic image data and the second image data istwo-dimensional Mediolateral Oblique (MLO) mammographic image data. 14.The method of claim 13, wherein determining whether the candidatedetection exists in both the first image data and the second image datacomprises analyzing the warped image output for correlated featuresassociated with the candidate detection, and wherein generating thedisplay output comprises generating an object identification on thewarped image.
 15. The method of claim 13, wherein the CNN is a fullyconvolutional network, wherein the CNN does not include any fullyconnected layer, and wherein the first image data and the second imagedata are input to the CNN in separate channels such that an output ofthe CNN comprises two channels defining row and column deformationcomponents.
 16. The method of claim 13, wherein the CNN comprises a skiparchitecture including one or more skip paths, and wherein each of theone or more skip paths generates a corresponding deformation field. 17.A method of identifying potential lesions in mammographic images, themethod comprising: receiving, by an image processing device, first imagedata of a first type; receiving, by the image processing device, secondimage data of a second type; identifying, by the image processingdevice, candidate regions via a first stage convolutional neural network(CNN) architecture configured to independently analyze the first imagedata and the second image data; conducting, by the image processingdevice, pairwise evaluation of the candidate regions to determinewhether candidate detection exists; and determining, by the imageprocessing device, candidate matches via a second stage CNN architectureand generating display output illustrating the candidate matches. 18.The method of claim 17, wherein the first image data is two-dimensionalCraniocaudal (CC) mammographic image data and the second image data istwo-dimensional Mediolateral Oblique (MLO) mammographic image data,wherein employing the first stage CNN architecture comprises: providinga plurality of first images associated with the first image data to afirst region-based CNN (R-CNN) trained to identify first candidateregions; and providing a plurality of second images associated with thesecond image data to a second R-CNN trained to identify second candidateregions, and wherein the first and second candidate regions comprise thecandidate regions on which the pairwise evaluation is conducted.
 19. Themethod of claim 18, wherein employing the second stage CNN architecturecomprises providing the second stage CNN architecture with dataassociated with the first candidate regions and the second candidateregions, and distance from nipple information for each instance of thecandidate detection in the data associated with the first candidateregions and the second candidate regions.
 20. The method of claim 19,wherein generating the display output comprises generating an objectidentification on both the first images and the second images.